On-line Signature Verification based on Pen Inclination and Pressure Information

In this paper, the features that have personal characteristic using pen inclination and pressure information are discussed. Forging a pen inclination and pressure information is difficult because it is not visible. Four features using invisible information are proposed and their characteristics are discussed. Proposed features calculated by physical vector analysis are verified by SVC2004 database using DP matching algorithm. As a result, the new feature named Down improves the recognition rate and reliability. Average of correct verification rate is 94.57 % and variance is 0.667.DOI:http://dx.doi.org/10.11591/ijece.v2i4.47

Efficient on-line signature verification system

In this paper, a robust automatic on-line signature verification system is proposed. The effectiveness of any on-line signature verification system depends mainly on the robustness of the dynamic features use in the system. Inability to extract highly discriminative dynamic features from signature has been contributing to higher verification error-rates. On-line signature verification experiments are conducted on seven dynamic signature features extracted from signature trajectories. Three features are found to be highly discriminative in comparison with others. The proposed system incorporates these three features for signature verification. Verification is based on the average of all the distances obtain from the cross-alignment of the features. The proposed system is tested with quality signature samples and it has 0.5% error in rejecting skilled forgeries while rejecting only 0.25% of genuine signatures. These results are better in comparison with the results obtained from previous systems

HMM-based on-line signature verification: Feature extraction and signature modeling

This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 28.16 (2007): 2325 – 2334, DOI: 10.1016/j.patrec.2007.07.012A function-based approach to on-line signature verification is presented. The system uses a set of time sequences and Hidden Markov Models (HMMs). Development and evaluation experiments are reported on a subcorpus of the MCYT bimodal biometric database comprising more than 7,000 signatures from 145 subjects. The system is compared to other state-of-the-art systems based on the results of the First International Signature Verification Competition (SVC 2004). A number of practical findings related to feature extraction and modeling are obtained.This work has been supported by the Spanish projects TIC2003-08382-C05- 01 and TEC2006-13141-C03-03, and by the European NoE Biosecure

Designing a comprehensive system for analysis of handwriting biomechanics in relation to neuromotor control of handwriting

A comprehensive system for investigation of biomechanical and neuromuscular processes involved with producing handwriting and drawing was developed. The system included a pen-like grip measuring device that enabled the variations of finger grip force associated with writing and drawing to be measured while holding the pen in tripod grip. The pen was integrated with a digitiser tablet for recording x,ycoordinates and pressure of the nib and a motion analysis system for recording the limb and hand kinematics. It was observed that for line drawing in the y-direction of the tablet, finger forces were directly related to pen tip movement and finger forces were modulated in a repeatable and predictable fashion, while this was not the case for line drawing in the x-direction. This was evidence for longstanding assumptions. Wrist rotation was required for production of lines in the x-direction without excessive deviation. For writing tasks, it was observed that no two tasks performed by one subject share an identical writing process, not even when the writing results are (nearly) identical. The neuromuscular control apparatus is highly flexible and works in a coordinated fashion that allows production of nearly equal end-results by means of different mechanical and therefore neuromuscular processes. For spiral drawing, tremor that originates from the fingers, hand and arm was quantified with the transducer pen. Limb joint kinematics were displayed in three dimensions with colour coding of coordinate sample numbers. This method can reveal the origin of some forms of limb tremor. Pen grip force patterns during signature writing were found to be characteristic for subjects, which relate to their individual pen-hand interaction, resulting from fine control of distal joints. Variation between trials of the same subject was observed, revealing adaptations of the computational processes during writing. The potential for signature verification by means of finger force recording was explored.A comprehensive system for investigation of biomechanical and neuromuscular processes involved with producing handwriting and drawing was developed. The system included a pen-like grip measuring device that enabled the variations of finger grip force associated with writing and drawing to be measured while holding the pen in tripod grip. The pen was integrated with a digitiser tablet for recording x,ycoordinates and pressure of the nib and a motion analysis system for recording the limb and hand kinematics. It was observed that for line drawing in the y-direction of the tablet, finger forces were directly related to pen tip movement and finger forces were modulated in a repeatable and predictable fashion, while this was not the case for line drawing in the x-direction. This was evidence for longstanding assumptions. Wrist rotation was required for production of lines in the x-direction without excessive deviation. For writing tasks, it was observed that no two tasks performed by one subject share an identical writing process, not even when the writing results are (nearly) identical. The neuromuscular control apparatus is highly flexible and works in a coordinated fashion that allows production of nearly equal end-results by means of different mechanical and therefore neuromuscular processes. For spiral drawing, tremor that originates from the fingers, hand and arm was quantified with the transducer pen. Limb joint kinematics were displayed in three dimensions with colour coding of coordinate sample numbers. This method can reveal the origin of some forms of limb tremor. Pen grip force patterns during signature writing were found to be characteristic for subjects, which relate to their individual pen-hand interaction, resulting from fine control of distal joints. Variation between trials of the same subject was observed, revealing adaptations of the computational processes during writing. The potential for signature verification by means of finger force recording was explored

A human computer interactions framework for biometric user identification

Computer assisted functionalities and services have saturated our world becoming such an integral part of our daily activities that we hardly notice them. In this study we are focusing on enhancements in Human-Computer Interaction (HCI) that can be achieved by natural user recognition embedded in the employed interaction models. Natural identification among humans is mostly based on biometric characteristics representing what-we-are (face, body outlook, voice, etc.) and how-we-behave (gait, gestures, posture, etc.) Following this observation, we investigate different approaches and methods for adapting existing biometric identification methods and technologies to the needs of evolving natural human computer interfaces

Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for online handwritten signature classification and verification based on descriptors stemming from Information Theory. The proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher Information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results produced surpass state-of-the-art techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.Comment: Submitted to PLOS On

Incorporating signature verification on handheld devices with user-dependent Hidden Markov Models

Proceedings of the International Conference on Frontiers in Hadwriting Recognition (ICFHR 2008)A dynamic signature verification system based on Hidden Markov Models is presented. For each user model, the number of states and Gaussian mixtures of the Hidden Markov Model is automatically set in order to optimize the verification performance. By introducing this userdependent structure in the statistical modeling of signatures, the system error rate is significantly decreased in the challenging scenario of dynamic signature verification on handheld devices. Experimental results are given on a subset of the recently acquired BIOSECURE multimodal database, using signatures captured with a PDAThis work has been supported by the Spanish Ministry of Education under project TEC2006-13141-C03-03

MULTI-MODEL BIOMETRICS AUTHENTICATION FRAMEWORK

Authentication is the process to conform the truth of an attribute claimed by real entity. Biometric technology is widely useful for the process of authentication. Today, biometric is becoming a key aspect in a multitude of applications. So this paper proposed the applications of such a multimodal biometric authentication system. Proposed system establishes a real time authentication framework using multi-model biometrics which consists of the embedded system verify the signatures, fingerprint and key pattern to authenticate the user. This is one of the most reliable, fast and cost effective tool for the user authentication